Measuring the morphology of brain sulci has been recently proposed as a novel imaging approach in Alzheimer's disease (AD). We aimed to investigate the relevance of such an approach in AD, by exploring its (1) clinical relevance in comparison with traditional imaging methods, (2) relationship with amyloid deposition, (3) association with cognitive functions. Here, 51 patients (n = 32 mild cognitive impairment/mild dementia-AD, n = 19 moderate/severe dementia-AD) diagnosed according to clinical-biological criteria (CSF biomarkers and amyloid-PET) and 29 controls (with negative amyloid-PET) underwent neuropsychological and 3T-MRI examinations. Mean sulcal width (SW) and mean cortical thickness around the sulcus (CT-S) were automatically measured. We found higher SW and lower CT-S in patients with AD than in controls. These differences were more pronounced at later stages of the disease and provided the best diagnostic accuracies among the imaging markers. Correlations were not found between CT-S or SW and amyloid deposition but between specific cognitive functions and regional CT-S/SW in key associated regions. Sulcal morphology is a good supporting diagnosis tool that reflects the main cognitive impairments in AD. It could be considered as a good surrogate marker to evaluate the efficacy of new drugs.

Cerebral white matter lesions (WMLs) reflect small vessel disease, are common in elderly individuals, and are associated with cognitive impairment. We sought to determine the relationships between WMLs, age, gray matter (GM) volume, and cognition in the Cardiovascular Health Study (CHS). From the Cardiovascular Health Study we selected 740 cognitively normal controls with a 1.5 T magnetic resonance imaging (MRI) scan of the brain and a detailed diagnostic evaluation. WML severity was determined using a standardized visual rating system. GM volumes were analyzed using voxel-based morphometry implemented in the Statistical Parametric Mapping software. WMLs were inversely correlated with GM volume, with the greatest volume loss in the frontal cortex. Age-related atrophy was observed in the hippocampus and posterior cingulate cortex. Regression analyses revealed links among age, APOE*4 allele, hypertension, WMLs, GM volume, and digit symbol substitution test scores. Both advancing age and hypertension predict higher WML load, which is itself associated with GM atrophy. Longitudinal data are needed to confirm the temporal sequence of events leading to a decline in cognitive function.

Several common genetic variants influence cholesterol levels, which play a key role in overall health. Myelin synthesis and maintenance are highly sensitive to cholesterol concentrations, and abnormal cholesterol levels increase the risk for various brain diseases, including Alzheimer's disease. We report significant associations between higher serum cholesterol (CHOL) and high-density lipoprotein levels and higher fractional anisotropy in 403 young adults (23.8 ± 2.4 years) scanned with diffusion imaging and anatomic magnetic resonance imaging at 4 Tesla. By fitting a multi-locus genetic model within white matter areas associated with CHOL, we found that a set of 18 cholesterol-related, single-nucleotide polymorphisms implicated in Alzheimer's disease risk predicted fractional anisotropy. We focused on the single-nucleotide polymorphism with the largest individual effects, CETP (rs5882), and found that increased G-allele dosage was associated with higher fractional anisotropy and lower radial and mean diffusivities in voxel-wise analyses of the whole brain. A follow-up analysis detected white matter associations with rs5882 in the opposite direction in 78 older individuals (74.3 ± 7.3 years). Cholesterol levels may influence white matter integrity, and cholesterol-related genes may exert age-dependent effects on the brain.

Poor kidney function is associated with increased risk of cognitive decline and generalized brain atrophy. Chronic kidney disease impairs glomerular filtration rate, and this deterioration is indicated by elevated blood levels of kidney biomarkers such as creatinine and cystatin C. Here we hypothesized that impaired renal function would be associated with brain deficits in regions vulnerable to neurodegeneration. Using tensor-based morphometry, we related patterns of brain volumetric differences to creatinine, cystatin C levels, and glomerular filtration rate in a large cohort of 738 (mean age, 75.5 ± 6.8 years; 438 men, 300 women) elderly Caucasian subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. Elevated kidney biomarkers were associated with volume deficits in the white matter region of the brain. All 3 renal parameters in our study showed significant associations consistently with a region that corresponds with the anterior limb of internal capsule, bilaterally. This is the first study to report a marked profile of structural alterations in the brain associated with elevated kidney biomarkers, helping us to explain the cognitive deficits.

The apolipoprotein E epsilon 4 allele (ApoE-ε4) is the strongest known genetic risk factor for late onset Alzheimer's disease. Expansion of the lateral ventricles occurs with normal aging, but dementia accelerates this process. Brain structure and function depend on ApoE genotype not just for Alzheimer's disease patients but also in healthy elderly individuals, and even in asymptomatic young individuals. Therefore, we hypothesized that the ApoE-ε4 allele is associated with altered patterns of longitudinal ventricular expansion, in dementia and normal aging. We tested this hypothesis in a large sample of elderly participants, using a linear discriminant analysis-based approach. Carrying more ApoE-ε4 alleles was associated with faster ventricular expansion bilaterally and with regional patterns of lateral ventricle morphology at 1- and 2-year follow up, after controlling for sex, age, and dementia status. ApoE genotyping is considered critical in clinical trials of Alzheimer's disease. These findings, combined with earlier investigations showing that ApoE is also directly implicated in other conditions, suggest that the selective enrollment of ApoE-ε4 carriers may empower clinical trials of other neurological disorders.

We investigated volume and shape changes in the striatum (caudate, putamen, and nucleus accumbens) of 18 early-onset (EOAD) and 18 late-onset (LOAD) Alzheimer's Disease patients compared with 2 control groups age- and sex-matched to each patient group, and explored the relationship between striatal atrophy and apolipoprotein E (APOE) genotype. EOAD patients showed significant shape changes in the left and right ventral putamen (p < 0.05, corrected for multiple comparison with permutation tests). LOAD patients showed significant reductions in the left and right nucleus accumbens volumes (p < 0.05; Mann-Whitney test) and shape (p < 0.05; permutation test). Caudate abnormalities were detected in EOAD and LOAD patients in terms of local enlargements and reductions (p < 0.05 for the left and right, permutation test). When APOE was considered, significant differences were detected between LOAD ε4 carriers and noncarriers in the left and right caudate (p < 0.05, permutation test). These results suggest distinct patterns of striatal pathology in EOAD and LOAD patients, the dorsal striatum being involved in EOAD and the ventral striatum in LOAD.

Cross-sectional studies show that elevated cerebral amyloid is associated with greater white-matter hyperintensity (WMH) burden in cognitively normal (CN) older adults. However, the relative time courses of amyloid and WMH accrual are unclear. To address this, we tested the associations between known WMH correlates-age, hypertension, and amyloid-with WMH accrual rate. We used brain magnetic resonance imaging to measure WMH change in 112 CN Alzheimer's Disease Neuroimaging Initiative (GO/2) participants over a 2-year period. A linear mixed effects model assessed baseline cerebrospinal fluid amyloid beta (Aβ) 1-42, hypertension, age, and their interactions, as predictors of greater WMH accrual. Greater amyloid burden was associated with greater WMH accrual over time. Those with hypertension showed a stronger association between greater amyloid burden and WMH accrual rate. Greater age was not significantly associated with greater WMH accrual in this model. Although the direction of the relationship cannot be tested in this model, CN individuals harboring cerebral amyloid had greater accrual of WMH over a 2-year period after accounting for hypertension and age. Impaired amyloid clearance and cerebral small vessel disease may both underlie the more rapid emergence of WM lesions. The role of cerebral amyloid burden in white-matter injury should thus be considered as a relevant factor when WMHs are detected clinically.

Occipital sources of resting-state electroencephalographic (EEG) alpha rhythms are abnormal, at the group level, in patients with amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD). Here, we evaluated the hypothesis that amplitude of these occipital sources is related to neurodegeneration in occipital lobe as measured by magnetic resonance imaging. Resting-state eyes-closed EEG rhythms were recorded in 45 healthy elderly (Nold), 100 MCI, and 90 AD subjects. Neurodegeneration of occipital lobe was indexed by weighted averages of gray matter density, estimated from structural MRIs. EEG rhythms of interest were alpha 1 (8-10.5 Hz) and alpha 2 (10.5-13 Hz). EEG cortical sources were estimated by low-resolution brain electromagnetic tomography. Results showed a positive correlation between occipital gray matter density and amplitude of occipital alpha 1 sources in Nold, MCI, and AD subjects as a whole group (r = 0.3, p = 0.000004, N = 235). Furthermore, there was a positive correlation between the amplitude of occipital alpha 1 sources and cognitive status as revealed by Mini Mental State Examination score across all subjects (r = 0.38, p = 0.000001, N = 235). Finally, amplitude of occipital alpha 1 sources allowed a moderate classification of individual Nold and AD subjects (sensitivity: 87.8%; specificity: 66.7%; area under the receiver operating characteristic curve: 0.81). These results suggest that the amplitude of occipital sources of resting-state alpha rhythms is related to AD neurodegeneration in occipital lobe along pathologic aging.

Ideal biomarkers of Alzheimer's disease (AD) should correlate with accepted measures of pathology in the cerebrospinal fluid (CSF); they should also correlate with, or predict, future clinical decline, and should be readily measured in hundreds to thousands of subjects. Here we explored the utility of automated 3D maps of the lateral ventricles as a possible biomarker of AD. We used our multi-atlas fluid image alignment (MAFIA) method, to compute ventricular models automatically, without user intervention, from 804 brain MRI scans with 184 AD, 391 mild cognitive impairment (MCI), and 229 healthy elderly controls (446 men, 338 women; age: 75.50 +/- 6.81 [SD] years). Radial expansion of the ventricles, computed pointwise, was strongly correlated with current cognition, depression ratings, Hachinski Ischemic scores, language scores, and with future clinical decline after controlling for any effects of age, gender, and educational level. In statistical maps ranked by effect sizes, ventricular differences were highly correlated with CSF measures of Abeta(1-42), and correlated with ApoE4 genotype. These statistical maps are highly automated, and offer a promising biomarker of AD for large-scale studies.

Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD.

A significant portion of our risk for dementia in old age is associated with lifestyle factors (diet, exercise, and cardiovascular health) that are modifiable, at least in principle. One such risk factor, high-homocysteine levels in the blood, is known to increase risk for Alzheimer's disease and vascular disorders. Here, we set out to understand how homocysteine levels relate to 3D surface-based maps of cortical gray matter distribution (thickness, volume, and surface area) computed from brain magnetic resonance imaging in 803 elderly subjects from the Alzheimer's Disease Neuroimaging Initiative data set. Individuals with higher plasma levels of homocysteine had lower gray matter thickness in bilateral frontal, parietal, occipital, and right temporal regions and lower gray matter volumes in left frontal, parietal, temporal, and occipital regions, after controlling for diagnosis, age, and sex and after correcting for multiple comparisons. No significant within-group associations were found in cognitively healthy people, patients with mild cognitive impairment, or patients with Alzheimer's disease. These regional differences in gray matter structure may be useful biomarkers to assess the effectiveness of interventions, such as vitamin B supplements, that aim to prevent homocysteine-related brain atrophy by normalizing homocysteine levels.

Brain connectivity is progressively disrupted in Alzheimer's disease (AD). Here, we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain's fiber networks from diffusion-weighted magnetic resonance imaging scans of 200 subjects from the Alzheimer's Disease Neuroimaging Initiative. We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of diffusion tensor imaging scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of mild cognitive impairment or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model-combining genotype, educational level, and clinical diagnosis.

The discovery of several genes that affect the risk for Alzheimer's disease ignited a worldwide search for single-nucleotide polymorphisms (SNPs), common genetic variants that affect the brain. Genome-wide search of all possible SNP-SNP interactions is challenging and rarely attempted because of the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, for example, iterative sure independence screening, make it possible to analyze data sets with vastly more predictors than observations. Using an implementation of the sure independence screening algorithm (called EPISIS), we performed a genome-wide interaction analysis testing all possible SNP-SNP interactions affecting regional brain volumes measured on magnetic resonance imaging and mapped using tensor-based morphometry. We identified a significant SNP-SNP interaction between rs1345203 and rs1213205 that explains 1.9% of the variance in temporal lobe volume. We mapped the whole brain, voxelwise effects of the interaction in the Alzheimer's Disease Neuroimaging Initiative data set and separately in an independent replication data set of healthy twins (Queensland Twin Imaging). Each additional loading in the interaction effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both Alzheimer's Disease Neuroimaging Initiative and Queensland Twin Imaging samples.

Dynamic changes in the brain's lateral ventricles on magnetic resonance imaging are powerful biomarkers of disease progression in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Ventricular measures can represent accumulation of diffuse brain atrophy with very high effect sizes. Despite having no direct role in cognition, ventricular expansion co-occurs with volumetric loss in gray and white matter structures. To better understand relationships between ventricular and cortical changes over time, we related ventricular expansion to atrophy in cognitively relevant cortical gray matter surfaces, which are more challenging to segment. In ADNI participants, percent change in ventricular volumes at 1-year (N = 677) and 2-year (N = 536) intervals was significantly associated with baseline cortical thickness and volume in the full sample controlling for age, sex, and diagnosis, and in MCI separately. Ventricular expansion in MCI was associated with thinner gray matter in frontal, temporal, and parietal regions affected by AD. Ventricular expansion reflects cortical atrophy in early AD, offering a useful biomarker for clinical trials of interventions to slow AD progression.

Reduction in hippocampal and amygdala volume measured via structural magnetic resonance imaging is an early marker of Alzheimer's disease (AD). Whether genetic risk factors for AD exert an effect on these subcortical structures independent of clinical status has not been fully investigated. We examine whether increased genetic risk for AD influences hippocampal and amygdala volumes in case-control and population cohorts at different ages, in 1674 older (aged >53 years; 17% AD, 39% mild cognitive impairment [MCI]) and 467 young (16-30 years) adults. An AD polygenic risk score combining common risk variants excluding apolipoprotein E (APOE), and a single nucleotide polymorphism in TREM2, were both associated with reduced hippocampal volume in healthy older adults and those with MCI. APOE ε4 was associated with hippocampal and amygdala volume in those with AD and MCI but was not associated in healthy older adults. No associations were found in young adults. Genetic risk for AD affects the hippocampus before the clinical symptoms of AD, reflecting a neurodegenerative effect before clinical manifestations in older adults.

This study modeled predementia Alzheimer's disease clinical trials. Longitudinal data from cognitively normal (CN) and mild cognitive impairment (MCI) participants in the Alzheimer's Disease Neuroimaging Initiative were used to calculate sample size requirements for trials using outcome measures, including the Clinical Dementia Rating scale sum of boxes, Mini-Mental State Examination, Alzheimer's Disease Assessment Scale-cognitive subscale with and without delayed recall, and the Rey Auditory Verbal Learning Task. We examined the impact on sample sizes of enrichment for genetic and biomarker criteria, including cerebrospinal fluid protein and neuroimaging analyses. We observed little cognitive decline in the CN population at 36 months, regardless of the enrichment strategy. Nonetheless, in CN subjects, using Rey Auditory Verbal Learning Task total as an outcome at 36 months required the fewest subjects across enrichment strategies, with apolipoprotein E genotype ε4 carrier status requiring the fewest (n = 499 per arm to demonstrate a 25% reduction in disease progression). In MCI, enrichment reduced the required sample sizes for trials, relative to estimates based on all subjects. For MCI, the Clinical Dementia Rating scale sum of boxes consistently required the smallest sample sizes. We conclude that predementia clinical trial conduct in Alzheimer's disease is enhanced by the use of biomarker inclusion criteria.

Late-onset and early-onset Alzheimer's disease (LOAD, EOAD) affect different neural systems and may be separate nosographic entities. The most striking differences are in the medial temporal lobe, severely affected in LOAD and relatively spared in EOAD. We assessed amygdalar morphology and volume in 18 LOAD and 18 EOAD patients and 36 aged-matched controls and explored their relationship with the hippocampal volume. Three-dimensional amygdalar shape was reconstructed with the radial atrophy mapping technique, hippocampal volume was measured using a manual method. Atrophy was greater in LOAD than EOAD: 25% versus 17% in the amygdala and 20% versus 13% in the hippocampus. In the amygdala, LOAD showed significantly greater tissue loss than EOAD in the right dorsal central, lateral, and basolateral nuclei (20%-30% loss, p < 0.03), all known to be connected to limbic regions. In LOAD but not EOAD, greater hippocampal atrophy was associated with amygdalar atrophy in the left dorsal central and medial nuclei (r = 0.6, p < 0.05) also part of the limbic system. These findings support the notion that limbic involvement is a prominent feature of LOAD but not EOAD.

The Alzheimer's Disease Neuroimaging Initiative recently implemented accelerated T1-weighted structural imaging to reduce scan times. Faster scans may reduce study costs and patient attrition by accommodating people who cannot tolerate long scan sessions. However, little is known about how scan acceleration affects the power to detect longitudinal brain change. Using tensor-based morphometry, no significant difference was detected in numerical summaries of atrophy rates from accelerated and nonaccelerated scans in subgroups of patients with Alzheimer's disease, early or late mild cognitive impairment, or healthy controls over a 6- and 12-month scan interval. Whole-brain voxelwise mapping analyses revealed some apparent regional differences in 6-month atrophy rates when comparing all subjects irrespective of diagnosis (n = 345). No such whole-brain difference was detected for the 12-month scan interval (n = 156). Effect sizes for structural brain changes were not detectably different in accelerated versus nonaccelerated data. Scan acceleration may influence brain measures but has minimal effects on tensor-based morphometry-derived atrophy measures, at least over the 6- and 12-month intervals examined here.

Alzheimer's disease (AD) is characterized by cortical atrophy and disrupted anatomic connectivity, and leads to abnormal interactions between neural systems. Diffusion-weighted imaging (DWI) and graph theory can be used to evaluate major brain networks and detect signs of a breakdown in network connectivity. In a longitudinal study using both DWI and standard magnetic resonance imaging (MRI), we assessed baseline white-matter connectivity patterns in 30 subjects with mild cognitive impairment (MCI, mean age 71.8 ± 7.5 years, 18 males and 12 females) from the Alzheimer's Disease Neuroimaging Initiative. Using both standard MRI-based cortical parcellations and whole-brain tractography, we computed baseline connectivity maps from which we calculated global "small-world" architecture measures, including mean clustering coefficient and characteristic path length. We evaluated whether these baseline network measures predicted future volumetric brain atrophy in MCI subjects, who are at risk for developing AD, as determined by 3-dimensional Jacobian "expansion factor maps" between baseline and 6-month follow-up anatomic scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression.

The goal of this work was to assess statistical power to detect treatment effects in Alzheimer's disease (AD) clinical trials using magnetic resonance imaging (MRI)-derived brain biomarkers. We used unbiased tensor-based morphometry (TBM) to analyze n = 5,738 scans, from Alzheimer's Disease Neuroimaging Initiative 2 participants scanned with both accelerated and nonaccelerated T1-weighted MRI at 3T. The study cohort included 198 healthy controls, 111 participants with significant memory complaint, 182 with early mild cognitive impairment (EMCI) and 177 late mild cognitive impairment (LMCI), and 155 AD patients, scanned at screening and 3, 6, 12, and 24 months. The statistical power to track brain change in TBM-based imaging biomarkers depends on the interscan interval, disease stage, and methods used to extract numerical summaries. To achieve reasonable sample size estimates for potential clinical trials, the minimal scan interval was 6 months for LMCI and AD and 12 months for EMCI. TBM-based imaging biomarkers were not sensitive to MRI scan acceleration, which gave results comparable with nonaccelerated sequences. ApoE status and baseline amyloid-beta positron emission tomography data improved statistical power. Among healthy, EMCI, and LMCI participants, sample size requirements were significantly lower in the amyloid+/ApoE4+ group than for the amyloid-/ApoE4- group. ApoE4 strongly predicted atrophy rates across brain regions most affected by AD, but the remaining 9 of the top 10 AD risk genes offered no added predictive value in this cohort.